A Text-Driven Framework for Corporate Risk Evaluation

Abstract

Text-driven intelligence has become an essential component of modern business analytics and risk management systems. However, effectively extracting actionable insights from large-scale unstructured corporate documents remains a challenging task due to linguistic diversity and contextual ambiguity. This study proposes a comprehensive text analytics framework for business risk assessment that integrates semantic representation learning and ensemble classification strategies. The framework combines statistical feature extraction and distributed word embedding techniques to construct multi-level textual representations, enabling robust characterization of business-related narratives. To improve prediction reliability, multiple learning models, including distance-based, probabilistic, and tree-based classifiers, are jointly incorporated into an adaptive ensemble architecture. In addition, data balancing mechanisms and context-sensitive embedding optimization are employed to enhance model generalization and stability. Experimental evaluations demonstrate that the proposed framework achieves superior classification performance and robustness across diverse business analysis scenarios. The results indicate that integrated representation learning and ensemble optimization provide an effective foundation for intelligent financial and operational risk assessment systems.